About us

We're solving AI's hardest problem: alignment.

Encord is the data layer for Physical AI and enterprise, serving as core infrastructure for 300+ of the world's leading AI teams, including Woven by Toyota, Hitachi, Skydio, AbbVie and Dyna, Walden and Agility Robotics. From autonomous vehicles and robotics to sports and healthcare, we're solving AI's hardest problem: turning complex, real-world data into production-ready intelligent systems.

We're a team of founders, scientists, quants, researchers, and former Olympians, who’ve helped build some of the most successful companies in Silicon Valley, and alumni of MIT, Stanford, UC Berkeley, Oxford, Cambridge, and Imperial. We’ve raised $110M from legendary technology investors, early backers of companies like Databricks, ElevenLabs and Vercel, and we’re growing at an extraordinary pace.

We're looking for exceptional people who want to help build a category-defining company, operating with high urgency and taking real ownership to help shape the future of AI.

The role

We're looking for a Principal Engineer to join our Foundations team, the backend backbone that builds the reliable, scalable, fast systems everyone else at Encord builds on top of. The team's core challenges sit deep in distributed and data intensive systems.

The product mindset on this team is different from most of the company: short-term product insight matters less, long-term matters a lot. Our Foundations team plans around how the next 6, 12, and 24 months will look, with an overarching shape to the problem: handling multiple orders of magnitude the data we handle today. This is our most senior individual contributor role. You'll own the hardest architectural calls, make the trade-offs that shape the platform for years, and raise the engineering bar across the company.

Encord is growing rapidly, with our product as the key driver of that growth. A year ago, we were a team of 100, now, we're over 150 and continuing to accelerate. The systems that got us here won't get us to what's next. That's the problem you'll own.

What you'll do

Design and lead major database and processing-system initiatives

Design performant protocols and APIs, such as our label-format API, that customers use directly. This work sits across both product and engineering

Build robust distributed systems and reason rigorously about how they fail and how they behave under load

Level up engineers across the company through design reviews, pairing, and mentorship, without adding process for its own sake

Who we're looking for

You've seen and built more than most. You've personally designed, built, and operated large-scale distributed systems in production, and you've lived with your architectural decisions long enough to know which trade-offs actually matter

Your impact shows up in the output of the engineers around you, not just your own commits. You make hard things look simple and teach others to do the same

You want your work to have a tangible outcome for people. You're not satisfied with building products that do not see the light of day

You are a builder. You enjoy all aspects of building not only software but a complete product, and are comfortable moving across the stack when necessary

You enjoy problem-solving and thinking from first principles, reach for the simplest solution that works, and know when boring technology wins

You're happy to own problems without a predefined, clear definition or obvious solution, and are excited to have autonomy to make decisions as you go

You understand that being a member of a high-performing team requires two things. You contribute your best work and help others contribute their best. You'll always lend a helping hand when needed and level up those around you

Experience requirements

10+ years building large-scale backend infrastructure applications, including several years operating at staff/principal level with company-wide technical influence

Deep expertise in distributed systems (databases, processing systems, replication, partitioning, queuing, caching) and a rigorous understanding of how they fail and behave under load at scale